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Record W2097480037 · doi:10.1109/jlt.2011.2160615

All-Optical Multihop Free-Space Optical Communication Systems

2011· article· en· W2097480037 on OpenAlexaff
Shabnam Kazemlou, Steve Hranilovic

Bibliographic record

VenueJournal of Lightwave Technology · 2011
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBit error rateRelayFree-space optical communicationOptical communicationTransmission (telecommunications)Channel (broadcasting)Electronic engineeringComputer scienceNoise (video)Signal-to-noise ratio (imaging)Field (mathematics)TelecommunicationsElectrical engineeringPhysicsEngineeringMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

All-optical relaying techniques are proposed to improve the error performance and overall distance coverage of free-space optical (FSO) communication systems. An all-optical amplify-and-forward (OAF) relaying technique is presented where the received optical field is amplified at each relay. A novel channel model is developed including field distributions and weak turbulence. Simulation results indicate that OAF significantly enhances the BER performance, but is severely degraded by background light. In order to remove the impact of background noise, an optical regenerate-and-forward (ORF) relaying technique is also presented. At a bit rate of 10 Gbps, using two equally-spaced OAF relays under a turbulence-free atmospheric condition increases the total communicating distance by 0.9 km over direct transmission at a BER of 10-5, while using two ORF relays provides an additional gain in range of 1.9 km. In general, replacing OAF relays by ORF relays extends the total communicating distance at a cost of implementation complexity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.235
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations124
Published2011
Admission routes1
Has abstractyes

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